Healthcare Monitoring System Modernization
Replatforming of cardiac monitoring infrastructure with AI integration
Client: Cardiac Health Monitoring Provider, USA
2
new AI-driven use cases enabled
MLOps
feature store, model registry, continuous training
Cloud-native
microservices in place of the monolith
Challenge
The legacy monolith was hard to scale and hard to maintain: infrastructure limits, fragmented data and no interoperability, which kept both AI use cases and new partners out. New markets and modern analytics were blocked by the same centralized architecture that was driving operating costs.
Solution
Replatformed to cloud-native microservices on Azure with event-driven architecture, AI capability and the healthcare exchange standards the domain runs on (FHIR, HL7v2, DICOM). A new ML platform carries the AI use cases with full MLOps and model lifecycle management.
Implementation
- 01Multi-stage migration using strangler and abstraction patterns
- 02Data consolidated into Azure Data Lake from on-prem and legacy sources
- 03ML platform with feature store, model registry and continuous training
- 04Bi-directional data exchange over FHIR, HL7v2 and DICOM
- 05GitOps, trunk-based development, IaC and canary deployments
Business impact
A scalable, modular system ready for new features and markets
2 new AI-driven use cases enabled by the ML platform
Better partner integrations and data exchange
Lower costs and time-to-market through modern DevOps
Clinical and operational data consolidated for analytics
Technology stack
Have a complex system to build or modernize?
Tell us what must change. We will bring the relevant domain and engineering leads into the conversation.